We quantify how well column-integrated CO<sub>2</sub> measurements from the
Orbiting Carbon Observatory (OCO) should be able to constrain surface
CO<sub>2</sub> fluxes, given the presence of various error sources. We
use variational data assimilation to optimize weekly fluxes at a
2&deg;&times;5&deg; resolution (lat/lon) using simulated data averaged across
each model grid box overflight (typically every ~33 s). Grid-scale
simulations of this sort have been carried out before for OCO using
simplified assumptions for the measurement error. Here, we more accurately
describe the OCO measurements in two ways. First, we use new estimates of
the single-sounding retrieval uncertainty and averaging kernel, both
computed as a function of surface type, solar zenith angle, aerosol optical
depth, and pointing mode (nadir vs. glint). Second, we collapse the
information content of all valid retrievals from each grid box crossing into
an equivalent multi-sounding measurement uncertainty, factoring in both
time/space error correlations and data rejection due to clouds and thick
aerosols. Finally, we examine the impact of three types of systematic
errors: measurement biases due to aerosols, transport errors, and mistuning
errors caused by assuming incorrect statistics.
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When only random measurement errors are considered, both nadir- and
glint-mode data give error reductions over the land of ~45% for the
weekly fluxes, and ~65% for seasonal fluxes. Systematic errors
reduce both the magnitude and spatial extent of these improvements by about
a factor of two, however. Improvements nearly as large are achieved over the
ocean using glint-mode data, but are degraded even more by the systematic
errors. Our ability to identify and remove systematic errors in both the
column retrievals and atmospheric assimilations will thus be critical for
maximizing the usefulness of the OCO data.